Information processing apparatus determining group corresponding to input spectral data

ABSTRACT

An error calculation unit calculates a difference between approximate spectral data obtained by approximating input spectral data using principal component data and the input spectral data for each of a plurality of groups. An error determination unit determines to which of a plurality of groups the input spectral data belongs based on a comparison result by the error calculation unit.

BACKGROUND OF THE INVENTION

1. Field of the Invention

One of the aspects of present invention relates to a technique forcategorizing input spectral data into a group having closecharacteristics.

2. Description of the Related Art

In recent years, devices handling images such as digital cameras,displays, and printers have undergone evolution, and a multi-primarycolor technique has been under development as a technology developmentrelating to color reproduction. Particularly in image input devices suchas digital cameras, technology development relating to a multi-bandcamera that takes a picture using four color filters or more exceedingthe number of conventional three primary colors (red, green, and blue)color filters is in progress.

One advantage of the technology to take a picture of a subject by amulti-band camera is that spectral data of the subject, that is, thespectral reflectance can be estimated with high precision. The spectralreflectance is subject-specific color information that does not dependon illumination information, and coloring of the subject under anillumination light source that is different from the light source whenan image is captured can be estimated with high precision by acquiringspectral data.

In addition to color reproduction, a detailed analysis of colorinformation can be performed using the spectral data. FIG. 10illustrates an example of two spectral reflectances. The spectralreflectances illustrated in FIG. 10 match in tristimulus values under aspecific light source (D50). In other words, colors cannot bedistinguished when XYZ values are compared. However, as is evident fromFIG. 10, two pieces of spectral data are significantly different and sodifferences of colors that cannot be determined by using the XYZ valuescan be determined by analyzing the spectral data.

According to Japanese Patent Application Laid-Open No. 2006-53070, thetypes of pigments used in coloring of the cultural properties areanalyzed by the spectral data in color analysis of cultural properties.Identification of coloring materials used in coloring of the culturalproperties can be effective information for, for example, recovery offading or restoration work.

When compared with the RGB values, XYZ values, or Lab values, spectraldata has a huge amount of data (dimensionality) needed to represent onecolor. While the RGB values, XYZ values, and Lab values arethree-dimensional data representing one color by three values of X, Y,and Z, spectral data is 31-dimensional data when the wavelength range of400 nm to 700 nm is sampled at intervals of 10 nm.

According to the technology discussed in Japanese Patent ApplicationLaid-Open No. 2006-53070, pigments used for coloring of a colored memberare estimated by comparing the spectral reflectance of the coloredmember with spectral data stored in advance. However, spectral data hasa huge dimensionality when compared with tristimulus value data such asXYZ data and thus, the spectral reflectance of the colored member isconsidered to rarely match the spectral data stored in advance.Therefore, an erroneous determination may be made if the spectralreflectances are simply compared.

SUMMARY OF THE INVENTION

According to an aspect of the present invention, an informationprocessing apparatus for determining a group corresponding to inputspectral data input thereto from among a plurality of groups includes aninput unit configured to input principal component data obtained byperforming a principal component analysis on a plurality of pieces ofreference spectral data for each of the plurality of groups, acalculation unit configured to calculate a difference betweenapproximate spectral data obtained by approximating the input spectraldata using the principal component data and the input spectral data foreach of the plurality of groups, and a determination unit configured todetermine a group corresponding to the input spectral data from thedifference calculated by the calculation unit.

According to one of the aspects of the present invention, for example,input spectral data can be categorized with high precision.

Further features and aspects of the present invention will becomeapparent from the following detailed description of exemplaryembodiments with reference to the attached drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of the specification, illustrate exemplary embodiments, features,and aspects of the invention and, together with the description, serveto explain the principles of the invention.

FIG. 1 illustrates a configuration of an image processing apparatusaccording to a first exemplary embodiment of the present invention.

FIG. 2 is a flow chart illustrating processing of the image processingapparatus according to the first exemplary embodiment.

FIGS. 3A and 3B illustrate reference spectral data of groups A and Baccording to an exemplary embodiment.

FIGS. 4A and 4B illustrate up to third principal components of principalcomponent data calculated from the reference spectral data of the groupsA and B according to an exemplary embodiment.

FIG. 5 illustrates input spectral data according to an exemplaryembodiment.

FIGS. 6A and 6B illustrate approximate results when the principalcomponent data calculated from the reference spectral data of the groupsA and B is used according to an exemplary embodiment.

FIG. 7 illustrates changes in root mean square (RMS) error values whenthe number of principal components to approximate the input spectraldata is changed according to an exemplary embodiment.

FIG. 8 illustrates a configuration of an image processing apparatusaccording to a second exemplary embodiment.

FIG. 9 is a flow chart illustrating the processing of the imageprocessing apparatus according to the second exemplary embodiment.

FIG. 10 illustrates an example of two spectral reflectances according toan exemplary embodiment.

DESCRIPTION OF THE EMBODIMENTS

Various exemplary embodiments, features, and aspects of the inventionwill be described in detail below with reference to the drawings.

FIG. 1 illustrates a configuration of an image processing apparatusaccording to a first exemplary embodiment. In FIG. 1, an imageprocessing apparatus 1 can categorize spectral data. An input spectraldata storage unit 2 stores input spectral data to be categorized. Areference spectral data storage unit 3 stores reference spectral datapieces which are separated into a plurality of groups in advance.

A spectral data input unit 4 reads the input spectral data stored in theinput spectral data storage unit 2 and the reference spectral datastored in the reference spectral data storage unit 3. A principalcomponent analysis unit 5 performs a principal component analysis of thereference spectral data input from the spectral data input unit 4. Aprincipal component data storage unit 6 stores principal component datacalculated by the principal component analysis unit 5. An input spectraldata approximation unit 7 targets the input spectral data input from thespectral data input unit 4 and calculates approximate spectral datausing the principal component data stored in the principal componentdata storage unit 6.

An error calculation unit 8 compares the input spectral data input fromthe spectral data input unit 4 and the approximate spectral datacalculated by the input spectral data approximation unit 7 to calculatean error therebetween. An error determination unit 9 determines whetherthe error calculated by the error calculation unit 8 is the minimumamong errors calculated heretofore. A determination result storage unit10 stores a determination result determined by the error determinationunit 9.

A determination result display apparatus 11 is a cathode ray tube (CRT),a liquid crystal display (LCD) monitor, or the like and displays thedetermination result. A determination result output unit 12 outputs thedetermination result stored in the determination result storage unit 10to the determination result display apparatus 11. The image processingapparatus 1 is configured as an application example of an informationprocessing apparatus. The input spectral data is an example of firstspectral data and the reference spectral data is an example of secondspectral data.

FIG. 2 is a flow chart illustrating processing of the image processingapparatus 1 according to the first exemplary embodiment. The processingof the image processing apparatus 1 according to the first exemplaryembodiment will be described in detail below with reference to FIG. 2.

In step S201, the spectral data input unit 4 reads input spectral datato be determined which is stored in the input spectral data storage unit2. In step S202, the spectral data input unit 4 sets a group to becompared to an initial value (i=0).

In step S203, the spectral data input unit 4 reads reference spectraldata belonging to the group (i-th group) set in step S202 from among thereference spectral data pieces stored in the reference spectral datastorage unit 3.

In step S204, the principal component analysis unit 5 calculatesprincipal component data by performing the principal component analysisof the reference spectral data read in step S203 and stores theprincipal component data in the principal component data storage unit 6.In step S205, the input spectral data approximation unit 7 targets theinput spectral data input in step S201 and calculates approximatespectral data by approximation using the principal component datacalculated in step S204.

In step S206, the error calculation unit 8 calculates an error Err_(—i)between the input spectral data and the approximate spectral datacalculated in step S205.

In step S207, the error determination unit 9 compares the error Err_(—i)calculated in step S206 and errors (Err_(—0) to Err_(—(i-1))) in eachgroup calculated heretofore to determine whether the error Err_(—i) isthe minimum from a comparison result. If the error Err_(—i) is theminimum (YES in step S207), the processing proceeds to step S208. On theother hand, if the error Err_(—i) is not the minimum (NO instep S207),the processing proceeds to step S209.

In step S208, the error determination unit 9 updates the group intowhich the input spectral data is categorized to a group i currentlybeing processed. More specifically, the error determination unit 9associates and stores the number (i) of the group currently beingprocessed and the error (Err_(—i)) in the determination result storageunit 10.

In step S209, the error determination unit 9 determines whether thereference spectral data pieces of all groups stored in the referencespectral data storage unit 3 have been compared (whether the processingin step S203 and thereafter has been performed thereon). If thereference spectral data pieces of all groups have been compared (YES instep S209), the processing proceeds to step S211. On the other hand, ifthe reference spectral data pieces of all groups have not been compared(NO in step S209), the processing proceeds to step S210.

In step S210, the spectral data input unit 4 changes the group to becompared to an unprocessed group, and the processing returns to stepS203. In step S211, the determination result output unit 12 outputs thegroup stored in the determination result storage unit 10 and whose erroris the minimum to the determination result display apparatus 11 as thegroup to which the input spectral data belongs.

Details of the calculation method of the approximate spectral data instep S205 will be described. In the present exemplary embodiment, inputspectral data itself is used as a characteristic amount of the inputspectral data. Approximate spectral data obtained by approximating theinput spectral data using lower-order principal component data ofprincipal component data calculated by principal component analysis ofthe reference spectral data is used as a characteristic amount of thereference spectral data.

First, the method for approximating input spectral data O represented byFormula 1 by principal component data E represented by Formula 2 will bedescribed.

$\begin{matrix}{O = \begin{bmatrix}O_{400} \\O_{410} \\O_{420} \\\vdots \\O_{700}\end{bmatrix}^{T}} & \left\lbrack {{Formula}\mspace{14mu} 1} \right\rbrack\end{matrix}$

-   O_(λ): Reflectance of input spectral data at wavelength λ

$\begin{matrix}{E = \begin{bmatrix}e_{1,400} & e_{1,410} & e_{1,420} & \ldots & e_{1,700} \\e_{2,400} & e_{2,410} & e_{2,420} & \ldots & e_{2,700} \\e_{3,400} & e_{3,410} & e_{3,420} & \ldots & e_{3,700} \\\vdots & \vdots & \vdots & \ddots & \vdots \\e_{31,400} & e_{31,410} & e_{31,420} & \ldots & e_{31,700}\end{bmatrix}} & \left\lbrack {{Formula}\mspace{14mu} 2} \right\rbrack\end{matrix}$

-   e_(n,λ): Reflectance of the n-th principal component at wavelength λ

The input spectral data O can be represented by Formula 3 as a linearsum of the principal component data E. If the input spectral data andthe reference spectral data are 31-dimensional data, principal componentdata can be calculated up to the 31st principal component, and anapproximation error from the input spectral data becomes 0 by using allcomponents up to the 31st principal component.

$\begin{matrix}\begin{matrix}{O = {\begin{bmatrix}O_{400} \\O_{410} \\O_{420} \\\vdots \\O_{700}\end{bmatrix}^{T} = {AE}}} \\{= \begin{bmatrix}{\left( {a_{1,400} \times e_{1,400}} \right) + \left( {a_{2,400} \times e_{2,400}} \right) + \ldots + \left( {a_{31,400} \times e_{31,400}} \right)} \\{\left( {a_{1,410} \times e_{1,410}} \right) + \left( {a_{2,410} \times e_{2,410}} \right) + \ldots + \left( {a_{31,410} \times e_{31,410}} \right)} \\{\left( {a_{1,420} \times e_{1,420}} \right) + \left( {a_{2,420} \times e_{2,420}} \right) + \ldots + \left( {a_{31,420} \times e_{31,420}} \right)} \\{\left( {a_{1,700} \times e_{1,700}} \right) + \left( {a_{2,700} \times e_{2,700}} \right) + \ldots + \left( {a_{31,700} \times e_{31,700}} \right)}\end{bmatrix}^{T}}\end{matrix} & \left\lbrack {{Formula}\mspace{14mu} 3} \right\rbrack\end{matrix}$The coefficient A in Formula 3 can be calculated by Formula 4 using themethod of least squares.A=[OE ^(T) ][EE ^(T)]⁻¹  [Formula 4]

If a case when the order of the principal component can be used up tothe N-th principal component (N<31) is considered, Formula 3 becomes alinear sum up to the N-th principal component and is represented byFormula 5.

$\begin{matrix}\begin{matrix}{O = {\begin{bmatrix}O_{400} \\O_{410} \\O_{420} \\\vdots \\O_{700}\end{bmatrix}^{T} = {BE}}} \\{= \begin{bmatrix}{\left( {b_{1,400} \times e_{1,400}} \right) + \left( {b_{2,400} \times e_{2,400}} \right) + \ldots + \left( {b_{N,400} \times e_{N,400}} \right)} \\{\left( {b_{1,410} \times e_{1,410}} \right) + \left( {b_{2,410} \times e_{2,410}} \right) + \ldots + \left( {b_{N,410} \times e_{N,410}} \right)} \\{\left( {b_{1,420} \times e_{1,420}} \right) + \left( {b_{2,420} \times e_{2,420}} \right) + \ldots + \left( {b_{N,420} \times e_{N,420}} \right)} \\{\left( {b_{1,700} \times e_{1,700}} \right) + \left( {b_{2,700} \times e_{2,700}} \right) + \ldots + \left( {b_{N,700} \times e_{N,700}} \right)}\end{bmatrix}^{T}}\end{matrix} & \left\lbrack {{Formula}\mspace{14mu} 5} \right\rbrack\end{matrix}$

In Formula 5, like in Formula 3, the coefficient B in Formula 5 can becalculated by Formula 6 using the method of least squares. However, theprincipal component data E used in Formula 6 is data of up to the N-thprincipal component (N<31).B=[OE ^(T) ][EE ^(T)]⁻¹  [Formula 6]

In Formula 5, since the order of the principal component is N (N<31), anerror arises when the input spectral data O is approximated. In thiscase, the error becomes smaller with increasingly closer characteristicsof the reference spectral data from which principal component data iscalculated and the input spectral data and thus, the input spectral datais considered to be likely to belong to a group of the referencespectral data used when the principal component data with a smallererror is calculated.

The accuracy of approximation of input spectral data when principalcomponent data calculated from reference spectral data of differentgroups is used will be described. In the description, spectralreflectances of leaves contained in Standard object color spectraldatabase for color reproduction evaluation (SOCS) (JIS-TR X 0012) areused as a concrete example.

Forty-six colors are extracted from spectral reflectances of 92 colorsthereof to use as reference spectral data pieces of a Group A. Further,the reference spectral data pieces of 46 colors of the Group A andreference spectral data of print output whose colors match (XYZ valuesof tristimulus values match) under a D50 light source are set as thereference spectral data of a Group B. The reference spectral data of theGroup A is illustrated in FIG. 3A and the reference spectral data of theGroup B is illustrated in FIG. 3B.

Further, principal components up to the third principal component ofprincipal component data calculated from the reference spectral data ofthe Group A are illustrated in FIG. 4A. Principal components up to thethird principal component of principal component data calculated fromthe reference spectral data of the Group B are illustrated in FIG. 4B.

Spectral reflectances of reprincipaling 46 colors excluding 46 colorsextracted as the reference spectral data from the spectral reflectancesof 92 colors of leaves in the SOCS database are input as input spectraldata. The input spectral data pieces are illustrated in FIG. 5.

Results of approximating the input spectral data by Formula 5 wherein Nis set to 3 are illustrated in FIGS. 6A and 6B. FIG. 6A illustrates anapproximation result (approximate spectral data) when the principalcomponent data calculated from the reference spectral data of the GroupA (spectral reflectances of leaves) is used with respect to one-colordata in the spectral reflectances illustrated in FIGS. 6A and 6B. FIG.6B illustrates an approximation result (approximate spectral data) whenthe principal component data calculated from the reference spectral dataof the Group B (spectral reflectances of prints) is used. It is evidentfrom FIGS. 6A and 6B that when the spectral reflectances of leaves areused as the input spectral data, approximations can be made moreprecisely if the principal component data calculated from the referencespectral data of the Group A containing the spectral reflectances ofleaves, which corresponds to the input spectral data, is used.

The error calculation unit 8 calculates an error between approximatespectral data approximated by targeting the input spectral data and theinput spectral data itself. As the calculation method of an error, forexample, using an RMS error as represented by Formula 7 can beconsidered.

$\begin{matrix}{{Err}_{\_\; i} = \sqrt{\frac{\sum\limits_{\lambda = 400}^{700}\left( {o_{\lambda} - r_{\lambda}} \right)^{2}}{31}}} & \left\lbrack {{Formula}\mspace{14mu} 7} \right\rbrack\end{matrix}$

-   o_(λ): Reflectance of input spectral data at wavelength λ-   r_(λ): Reflectance of approximate spectral data at wavelength λ

In the above exemplary embodiment, however, the calculation method of anerror is not limited to the RMS error of input spectral data andapproximate spectral data. If, for example, as illustrated in FIG. 7,the number of principal components used for approximation is changed,the RMS error also changes. Thus, for example, an arbitrary thresholdmay be set to the RMS error to set the minimum number of principalcomponents that can make approximations with an error below thethreshold as an error value. In other words, a method for forming thesmallest group with the minimum number of principal components below thethreshold can be considered. Alternatively, a method for using a totalsum of RMS errors when the number of principal components is changed asan error value can be considered.

Next, a second exemplary embodiment will be described. In the firstexemplary embodiment, input spectral data itself is used as acharacteristic amount of the input spectral data. Also in the firstexemplary embodiment, approximate spectral data obtained byapproximating input spectral data by using only lower-order principalcomponent data of principal component data calculated by principalcomponent analysis of reference spectral data is used as acharacteristic amount of the reference spectral data. In the secondexemplary embodiment, by contrast, a method will be described which usesdifferences of reflectances at a plurality of wavelengths set in advanceby a user as characteristic amounts of input spectral data and referencespectral data.

FIG. 8 illustrates a configuration of an image processing apparatus 801according to the second exemplary embodiment. An input spectral datastorage unit 802, a reference spectral data storage unit 803, and aspectral data input unit 804 in FIG. 8 are the same as the inputspectral data storage unit 2, the reference spectral data storage unit3, and the spectral data input unit 4 in FIG. 1 respectively. Further, adetermination result storage unit 809, a determination result outputunit 811, and a determination result display apparatus 810 in FIG. 8 arethe same as the determination result storage unit 10, the determinationresult output unit 12 and the determination result display apparatus 11in FIG. 1 respectively. Therefore, the description of these componentswill not be repeated.

In FIG. 8, a characteristic amount calculation unit 805 calculatescharacteristic amounts of input spectral data and reference spectraldata which are input from the spectral data input unit 804. Acharacteristic amount storage unit 806 stores the characteristic amountscalculated by the characteristic amount calculation unit 805. Acharacteristic amount comparison unit 807 compares the characteristicamount of the input spectral data and that of the reference spectraldata stored in the characteristic amount storage unit 806 to calculatean error.

FIG. 9 is a flow chart illustrating the processing of the imageprocessing apparatus according to the second exemplary embodiment. Theprocessing of the image processing apparatus according to the secondexemplary embodiment will be described below with reference to FIG. 9.

In step S901, the spectral data input unit 804 reads input spectral datato be determined which is stored in the input spectral data storage unit802. In step S902, the characteristic amount calculation unit 805calculates a characteristic amount of the input spectral data read instep S901 and stores the characteristic amount in the characteristicamount storage unit 806.

In step S903, the spectral data input unit 804 sets a group to becompared to an initial value (i=0). In step S904, the spectral datainput unit 804 reads reference spectral data belonging to the group(i-th group) set in step S903 from among the reference spectral datastored in the reference spectral data storage unit 803.

In step S905, the characteristic amount calculation unit 805 calculatesa characteristic amount of the reference spectral data read in step S904and stores the characteristic amount in the characteristic amountstorage unit 806.

In step S906, the characteristic amount comparison unit 807 compares thecharacteristic amount of the input spectral data and that of thereference spectral data stored in the characteristic amount storage unit806 to calculate an error Err_(—i).

In step S907, an error determination unit 808 compares the errorErr_(—i) calculated in step S906 and errors (Err_(—0) to Err_(—(i-1)))in each group calculated heretofore to determine whether the errorErr_(—i) is the minimum. If the error Err_(—i) is the minimum (YES instep S907), the processing proceeds to step S908. On the other hand, ifthe error Err_(—i) is not the minimum (NO in step S907), the processingproceeds to step S909.

Subsequent steps S908 to S911 are similar to steps S207 to S211 in thefirst exemplary embodiment and thus, the description thereof is omitted.

Next, the method for calculating the characteristic amount of the inputspectral data in step S902 and the characteristic amount of thereference spectral data in step S905 will be described. Differencevalues between the reflectance at wavelength of 550 nm and thereflectance at wavelength of 650 nm of the input spectral data and thereference spectral data are used as the characteristic amounts.

If, as described above, the input spectral data O is represented byFormula 1, a characteristic amount Vin of the input spectral data, thatis, a difference between the reflectance at wavelength of 550 nm and thereflectance at wavelength of 650 nm is calculated by Formula 8. Thepresent processing is a processing example of a first calculation unit.V _(in) =o ₅₅₀ −o ₆₅₀

If M pieces of reference spectral data are grouped in the group i andspectral data of the m-th reference spectral data of the group i atwavelength λ is r_(i) _(—) _(m) _(—) λ, a characteristic amount V_(ref)_(—) _(i) of the group i is calculated by Formula 9.

$\begin{matrix}{V_{ref\_ i} = \frac{\sum\limits_{j = 1}^{M}\left( {r_{{i\_ m}\_ 550} - r_{{i\_ m}\_ 650}} \right)}{M}} & \left\lbrack {{Formula}\mspace{14mu} 9} \right\rbrack\end{matrix}$

More specifically, the characteristic amount represented by Formula 9 isan average value of respective characteristic amounts of M pieces ofspectral data contained in the reference spectral data. However, thecalculated characteristic amount is not limited to the valuesrepresented by Formulae 8 and 9. For example, an average value or avariance value of reference spectral data may be used or a weightedlinear sum of the average value, variance value, and a difference ofreflectances between specific wavelengths may be used as acharacteristic amount. The present processing is a processing example ofa second calculation unit.

Next, the method for calculating an error between the characteristicamount of the input spectral data and that of the reference spectraldata in step S906 is described. As described above, the characteristicamount comparison unit 807 calculates the error Err_(—i) of thecharacteristic amount by the characteristic amount V_(in) of the inputspectral data and the characteristic amount V_(ref) _(—) _(i) of thereference spectral data of the i-th group by Formula 10.Err_(—i)=√{square root over ((V _(in) −V _(ref) _(—) _(i))²)}  [Formula10]

In other words, the error Err_(—i) becomes smaller with increasinglycloser the characteristics of the input spectral data and the referencespectral data.

In the above described exemplary embodiments, spectral data to beprocessed is described as 31-dimensional data obtained by sampling thewavelength range of 400 nm to 700 nm at intervals of 10 nm, but needlessto say, the wavelength range and the wavelength interval are not limitedto the above examples. Any wavelength range and interval desired by theuser may be used and, for example, 401-dimensional spectral dataobtained by sampling the wavelength range of 380 nm to 780 nm atintervals of 1 nm may be used.

In the above described exemplary embodiments, the characteristic amountcalculated from input spectral data and that calculated from referencespectral data grouped in advance are compared to determine to whichgroup characteristics of the input spectral data belong. Therefore, evenwhen no reference spectral data matching input spectral data is present,it is possible to determine the group of the reference spectral datahaving characteristics close to the characteristics of the inputspectral data. Thus, the input spectral data can be categorized withhigh precision.

Aspects of the present invention can also be realized by a computer of asystem or apparatus (or devices such as a CPU or MPU) that reads out andexecutes a program recorded on a memory device to perform the functionsof the above-described embodiment(s), and by a method, the steps ofwhich are performed by a computer of a system or apparatus by, forexample, reading out and executing a program recorded on a memory deviceto perform the functions of the above-described embodiment(s). For thispurpose, the program is provided to the computer for example via anetwork or from a recording medium of various types serving as thememory device (e.g., computer-readable medium).

While the present invention has been described with reference toexemplary embodiments, it is to be understood that the invention is notlimited to the disclosed exemplary embodiments. The scope of thefollowing claims is to be accorded the broadest interpretation so as toencompass all modifications, equivalent structures, and functions.

This application claims priority from Japanese Patent Application No.2010-276125 filed Dec. 10, 2010, which is hereby incorporated byreference herein in its entirety.

What is claimed is:
 1. An information processing apparatus fordetermining a group corresponding to input spectral data input theretofrom among a plurality of groups, the information processing apparatuscomprising: an input unit configured to input principal component dataobtained by performing a principal component analysis on a plurality ofpieces of reference spectral data for each of the plurality of groups; acalculation unit configured to calculate a difference betweenapproximate spectral data obtained by approximating the input spectraldata using the principal component data and the input spectral data foreach of the plurality of groups; and a determination unit configured todetermine a group corresponding to the input spectral data from thedifference calculated by the calculation unit.
 2. The informationprocessing apparatus according to claim 1, wherein the determinationunit determines a group for which a difference calculated by thecalculation unit becomes a minimum as the group corresponding to theinput spectral data.
 3. The information processing apparatus accordingto claim 1, wherein the calculation unit calculates a difference betweenapproximate spectral data obtained by an approximation using principalcomponent data having less dimensionality than a dimensionality of thereference spectral data and the input spectral data.
 4. The informationprocessing apparatus according to claim 3, wherein the dimensionality ofthe reference spectral data is three.
 5. The information processingapparatus according to claim 1, wherein the difference is a root meansquare (RMS) error.
 6. The information processing apparatus according toclaim 1, wherein the calculation unit calculates a difference betweenapproximate spectral data obtained by an approximation using principalcomponent data including a minimum number of principal components thatare lower than a threshold and the input spectral data.
 7. Theinformation processing apparatus according to claim 1, wherein thedifference is a difference between a plurality of wavelengths.
 8. Amethod for information processing that determines a group correspondingto input spectral data that is input from among a plurality of groups,the method comprising: inputting principal component data obtained byperforming a principal component analysis on a plurality of pieces ofreference spectral data for each of the plurality of groups; calculatinga difference between approximate spectral data obtained by approximatingthe input spectral data using the principal component data and the inputspectral data for each of the plurality of groups; and determining agroup corresponding to the input spectral data from the calculateddifference.
 9. A non-transitive computer-readable storage medium storinga computer-executable program for determining a group corresponding toinput spectral data that is input from among a plurality of groups, theprogram comprising codes for: inputting principal component dataobtained by performing a principal component analysis on a plurality ofpieces of reference spectral data for each of the plurality of groups;calculating a difference between approximate spectral data obtained byapproximating the input spectral data using the principal component dataand the input spectral data for each of the plurality of groups; anddetermining a group corresponding to the input spectral data from thecalculated difference.